Skip to main content

Implementation of K-Mean Cluster

# Step 1: Import Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_wine

# Step 2: Load the Wine Dataset
wine = load_wine()
data = pd.DataFrame(wine.data, columns=wine.feature_names)
# Display the first few rows of the dataset
print("First few rows of the dataset:")
print(data.head())

# Step 3: Data Preprocessing
# Standardize the features
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)
print("\nStandardized Data (first few rows):")
print(scaled_data[:5])

# Step 4: Apply K-means Clustering
# Setting the number of clusters directly (e.g., k = 3)
k = 3
kmeans = KMeans(n_clusters=k, random_state=42)
clusters = kmeans.fit_predict(scaled_data)
# Add cluster labels to the original data
data['Cluster'] = clusters
# Print Centroid Values
centroids = kmeans.cluster_centers_
print("\nCentroid values (scaled):")
print(centroids)

# Step 5: Visualize the Clusters
# We will plot only the first two features for simplicity
plt.figure(figsize=(10, 6))
plt.scatter(scaled_data[:, 0], scaled_data[:, 1], c=clusters, cmap='viridis')
plt.scatter(centroids[:, 0], centroids[:, 1], s=300, c='red', marker='X', label='Centroids')
plt.title('K-means Clustering on Wine Dataset')
plt.xlabel('Feature 1 (alcohol)')
plt.ylabel('Feature 2 (malic_acid)')
plt.colorbar(label='Cluster')
plt.legend()
plt.show()

# Step 6: Analyze Results
# Print mean values of each feature per cluster
cluster_analysis = data.groupby('Cluster').mean()
print("\nMean values of each feature per cluster:")
print(cluster_analysis)

Output:



Mean values of each feature per cluster: alcohol malic_acid ash alcalinity_of_ash magnesium \ Cluster 0 12.250923 1.897385 2.231231 20.063077 92.738462 1 13.134118 3.307255 2.417647 21.241176 98.666667 2 13.676774 1.997903 2.466290 17.462903 107.967742 total_phenols flavanoids nonflavanoid_phenols proanthocyanins \ Cluster 0 2.247692 2.050000 0.357692 1.624154 1 1.683922 0.818824 0.451961 1.145882 2 2.847581 3.003226 0.292097 1.922097 color_intensity hue od280/od315_of_diluted_wines proline Cluster 0 2.973077 1.062708 2.803385 510.169231 1 7.234706 0.691961 1.696667 619.058824 2 5.453548 1.065484 3.163387 1100.225806


Comments

Post a Comment

Popular posts from this blog

Logistic Regression

Logistic regression is a statistical method used for binary classification problems. It's particularly useful when you need to predict the probability of a binary outcome based on one or more predictor variables. Here's a breakdown: What is Logistic Regression? Purpose : It models the probability of a binary outcome (e.g., yes/no, success/failure) using a logistic function (sigmoid function). Function : The logistic function maps predicted values (which are in a range from negative infinity to positive infinity) to a probability range between 0 and 1. Formula : The model is typically expressed as: P ( Y = 1 ∣ X ) = 1 1 + e − ( β 0 + β 1 X ) P(Y = 1 | X) = \frac{1}{1 + e^{-(\beta_0 + \beta_1 X)}} P ( Y = 1∣ X ) = 1 + e − ( β 0 ​ + β 1 ​ X ) 1 ​ Where P ( Y = 1 ∣ X ) P(Y = 1 | X) P ( Y = 1∣ X ) is the probability of the outcome being 1 given predictor X X X , and β 0 \beta_0 β 0 ​ and β 1 \beta_1 β 1 ​ are coefficients estimated during model training. When to Apply Logistic R...
Post a Comment
Read more

Linear Regression using Ordinary Least Square method

Ordinary Least Square Method Download Dataset Step 1: Import the necessary libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt Step 2: Load the CSV Data # Load the dataset data = pd.read_csv('house_data.csv') # Extract the features (X) and target variable (y) X = data['Size'].values y = data['Price'].values # Reshape X to be a 2D array X = X.reshape(-1, 1) # Add a column of ones to X for the intercept X_b = np.c_[np.ones((X.shape[0], 1)), X] Step 3: Add a Column of Ones to X for the Intercept # Add a column of ones to X for the intercept X_b = np.c_[np.ones((X.shape[0], 1)), X] Step 4: Implement the OLS Method # Calculate the OLS estimate of theta (the coefficients) theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y) Step 5: Make Predictions # Make predictions y_pred = X_b.dot(theta_best) Step 6: Visualize the Results # Plot the data and the regression line plt.scatter(X, y, color='blue', label='Data') plt.pl...
Post a Comment
Read more

Quadratic Regression

  Quadratic regression is a statistical method used to model a relationship between variables with a parabolic best-fit curve, rather than a straight line. It's ideal when the data relationship appears curvilinear. The goal is to fit a quadratic equation   y=ax^2+bx+c y = a ⁢ x 2 + b ⁢ x + c to the observed data, providing a nuanced model of the relationship. Contrary to historical or biological connotations, "regression" in this mathematical context refers to advancing our understanding of complex relationships among variables, particularly when data follows a curvilinear pattern. Working with quadratic regression These calculations can become quite complex and tedious. We have just gone over a few very detailed formulas, but the truth is that we can handle these calculations with a graphing calculator. This saves us from having to go through so many steps -- but we still must understand the core concepts at play. Let's try a practice problem that includes quadratic ...
Post a Comment
Read more